ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: CNV analysis with Daniel Howrigan
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1
Hello.
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My name is Daniel Harrigan, and today I'll be talking about how to run copy number variation analysis.
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Today's talk, I'll address a couple of questions, namely, what is a copy number variant and how do we detect it with genotype data?
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What does the CNV file format look like?
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And what does CNV analysis output look like?
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Finally, how do I use this to run CNV burden and association tests?
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So, what is a copy number variant?
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Well, I define it here as a subset of structural variation involving a gain
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or duplication or a loss slash deletion of genomic sequence.
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Now, I call it a subset of structural variation because structural variation
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can encompass basically any change in the length of a genomic sequence
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that can be as small as a single base pair insertion
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or deletion now most snips that we we think of are just substitutions
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so the actual size of the genome isn't changed with a single nucleotide polymorphism
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but a structural variant can encompass anything from very small all the way to entire chromosomes.
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What we call CNVs, while it being kind of a general name,
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falls into the category of roughly at least defined here at minimum one kilobase to submicroscopic.
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With array data, we generally say something around 10 to 100 kilobases, given our sensitivity to detect these CNVs,
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whereas larger CNVs usually are greater than 500 kilobases up to multiple megabases.
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And then we get into much larger events.
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Now, I'm not going to get into the details of the mechanisms that cause it.
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I've listed a few here.
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You can look them up.
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But there are different hotspots in the genome
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that are more prone to these copy number variants due to repeat regions
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or often ways in the machinery that can make mistakes and lead to these gains or losses of genomic sequence.
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How do we detect it using genotype data?
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So when we run across the genome collecting a bunch of SNPs, I show here kind of the basic mechanism
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or at least a figure showing how we take light intensity from different experiments looking at capturing either
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you know allele a i have aa as allele a
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or the b allele being tt here
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and then a heterozygous you can see a mix of both red
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and green now we can leverage these allele frequencies
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and what we call the b allele frequency
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or at least the frequency of of the t in this case now normally
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when there's no copy number variant these will be roughly equal
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and so will be around 50 percent
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when we see something like a large deletion or any sort of deletion we should see a loss of heterozygosity,
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and therefore we will see a gap in these B alleles,
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at least at any site that you would normally be heterozygote at.
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With the duplication, these would be a little bit off, so it would be more like two -thirds,
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one -third, and you would see maybe a movement kind of akin to where these pink lines may be going,
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even though they don't quite to find that you would see in a duplication
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that the b allele frequencies move off 50 but not to one
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and zero now that's looking at the b alleles the other thing
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we use to detect it is the log r ratio
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and this is basically measuring the light intensity and when you see a drop here
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where we would see maybe in in all of these calls you should see a drop of say 50 percent
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when we have a deletion and you will see subsequently a rise of about 33 percent
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for a duplication now i'm not going to get into the details of the colors
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that are used to detect these copy number variants suffice to say this is kind of the foundation that it's built upon
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What I'm going to talk about next is given
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that you've run copy number variant callers here are the kind of QC calls that you're moving into the analysis stage.
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Now, I use Plink to analyze the CNV data so you can get a bunch of different file formats,
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copy number variants from different callers.
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But you would let, you know, if you want to use Plink, you would convert this into this particular file format, which is the .cnv file.
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And it's basically individual identifiers, the chromosome, the start and end position of this CNV,
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the type, one being a deletion, three being a duplication.
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and then also there's a few other fields here the score
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and sites field in this example i use the score being say the number of copy number variant callers
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that agreed upon this given call
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and it could range up to six sites here being the number of snips that are used to call the cnv
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Now, I note here that score and sites are not forced into a particular convention.
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You could say replace score with the number of genes overlapping the CNV,
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or the sites being some other variable that you would be interested in measuring.
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Now along with this file, plink creates a .cnv .map file,
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and this is basically breaking down the break points of each
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cnv into akin to a map file similar to what we have for snip data
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and you can see here i note
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that every different position is mapped even the the end position
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but also maybe a single position after that end
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because you may want to do a test an additional final
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test after the end of a CNB to see how things have changed.
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Note that the CNB file format commands are not available in Plink 1 .9, but the initial version granted the speed ups
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that you get using the newer version of Plink aren't all
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that applicable here because we're generally dealing with rare variation and
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so these file sizes are generally not too large and the sorts of computing that you use is not too heavy.
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So what does CNV analysis output look like?
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Usually whenever you run a command in Flink looking at your CNV files, you get out .cnv .indiv file.
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So this is per sample file where you say the number of cnv segments
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that this individual has the number of kilobases that these segments cover
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and the average kilobases covered per segment you also get a cnv .summary file
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this is akin to the map file that is summarizing the number of affected
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and unaffected individuals at any given break point or start, end, and plus one of a CMB.
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And this is basically, you know, the output here, while it seems quite simple,
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I'll show how with these files you can do quite sophisticated analysis by using a lot of different filters.
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So a lot of the magic in Plink is all the flags that you can use to subset your list of CNVs.
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So what I have here is a kind of verbose command just to show the optionality available in Plink.
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And for each of these, I describe what that function is doing.
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So with Plink, you have dash dash C file.
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This reads in the CNV and CNV map file.
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I want to select only deletions.
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I want to select CNVs that are at least 100 kilobases in length.
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I want CNVs with a score of four or higher and at least 50 sites.
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I want to exclude CNVs that overlap a particular region.
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So I can insert a different text file with a list of chromosome and start -end positions here.
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And I want to make sure in this exclusion that the CNVs must overlap by at least 50 % to be excluded.
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I can also look at frequency where at least 10 CNVs overlap.
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I would like to exclude those because maybe I'm more interested in very rare.
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uh cnvs i can also write out the frequencies of of these of these cnvs as well just
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so i can guarantee which cnvs are being dropped
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which cnvs are being kept
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and then i can run a basic burden test using a permutation model
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and here i just set the number of permutations these 10 000
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so you can see there's a lot of different flags here and it's manipulating a lot of these flags
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that can give you just what you would like in terms of your analysis now granted
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using the burden tests in plank doesn't handle covariates it basically just looks at something like case control status
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and so what i recommend is taking the output particularly the cnd cnv .indiv file
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and reading that into say python or r i prefer r to run more sophisticated models
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and so you can see when you put more filters here
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if i go back the number of segments will change depending on what filters,
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and obviously, subsequently, the number of kilobases covered by these segments will change as a function.
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And it's kind of iteratively reading in these files at different filtering steps that can produce a wide range of tests.
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So I've shown a couple figures here that we published when looking at CNV burden in the PGC schizophrenia.
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So I took in those .CNV .indiv files into R.
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I ran a logistic regression predicting schizophrenia status and adding a number of covariates,
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such as principal components, genotype platforms, and then basically as I iteratively ran different
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uh commands in plank to look at kv cnv counts lengths frequencies whether
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or not they're in
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or not in particular regions you can build up a number of tests of overall burn
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so in this example here this would be all cnvs deletions
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and duplications stratified by different genotyping platforms and then all together and then b here i'm stratifying by different frequencies
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and say previously implicated cnvs as a region
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i want to say in the blue bars we're looking at enrichment here in the green
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and blue bars but you can see the big deviation here there's a lot of enrichment
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when we talk about cnvs at this size or at least this frequency i mean
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and they go away because most of these have been implicated
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so i've excluded these regions rerun the burden test
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and you can see that we've captured much of the signal already with previously implicated cnvs
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so how do we use this to run cnv association tests at individual cnv loci or at individual break points of CNVs.
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Basically, I would run a very similar command.
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I could use all the same filters, basically get rid of a number of commands.
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In particular, you're getting rid of this CNV indiv .perm step, but you still run a permutation test,
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and what you'll get out is a .cnv .summary .m perm file
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and at each base position you can run an association test here using permutation
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this would be the point wise permutation value
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but there's also a family wise permutation p -value
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that corrects for all the tests here
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so this association is run at all possible start
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and in n plus one positions and one of the things you can do
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if you like to include covariates in your data at least
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what i've done in the past is maybe run your logistic
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regression model with a lot of your covariates pull out the residuals
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and then use this as a quantitative trait and you can run association mapping in Plink to get p -values that way.
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So what does this look like?
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I think having a figure kind of is instructive here.
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So I plotted
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through a browser i'll break this down this is our signal at the norexin1 gene
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so in red we have our deletions uh light red deletions in our schizophrenia cases
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dark red deletions in our schizophrenia controls i also have duplications in blue
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which don't make up much of the signal here
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but as you can see i've also plotted the log negative log 10 p -value.
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And if you kind of look closely, you can see each little breakpoint, you can see a different test being run.
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And you can see at, you know, a spot like this where there's many different breakpoints,
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a lot of granularity, you can run many different tests and kind of get a shape of the association around this gene.
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Now you can also collapse.
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Another test that we've done is collapsing across say all the exons of this gene
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and so this would be akin more to like a gene burden test where you collapse this region
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and then you test for overlap at that region run a similar model
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and then you can kind of aggregate all the cnvs and cases and controls to report say a gene -based p -value.
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So that's a very quick overview of how to run burden and association with CNV data.
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And a few considerations is that, you know, one of the things that you don't have access to
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when looking at cnvs is imputation and so it is a consideration to think about you know
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there isn't kind of a reference uh you know reference haplotypes
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or a larger data set to do additional qc
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so there can be additional challenges particularly with subpar data
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that they can't be rescued in the way
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that imputation uh can rescue uh snip genotypes and on
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that note you know genotyping chip does matter very much
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because you can't impute a bunch of different new sites the
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variability in terms of the number of snips particularly for smaller cnvs is a very kind of important consideration
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and so you know you can think of in a particular genotyping chip
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if you don't have a good case control balance there you may not have the sensitivity to properly detect CNVs.
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And so there's a lot of work
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that goes into determining at what length of CNVs or at least at what case control balances given your genotyping chip,
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do you have the right amount of power and sensitivity to do a proper case control association
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Another thing too, ancestry PCs,
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most of the CNV hotspots and associated CNV that we see in psychiatric disease aren't very impacted,
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and mainly that's because these are recurrent de novo CNV areas where there's a higher mutation rate,
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but this is not really ancestrally defined in terms of the fact
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that there's not a large difference in the allele frequency of the CNV across different ancestries, but it is still useful to include,
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particularly as you get to higher frequencies and you get more inherited CNVs. And finally,
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multiple testing correction biopermutation is one of the more robust ways to account for the multiple tests
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because the nature of cnv data is such
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that given the type of genotyping chip
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that you use the size of your data set no no
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particular study is going to be very similar to similar to another study and
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that kind of leveraging the correlation structure within your own data set given
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you know your ability to detect CNVs of various sizes
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and frequencies using permutation is usually the best way to go about properly testing for association.
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So if you have any questions, feel free to email me.
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I've also put down some sites.
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You could search here for the PGC CNV paper that we did in 2017.
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And also, I think the write -up in PLINK on how to do this is really good
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and very descriptive of all its functionality.
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Thank you.
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดนี้ใช้วิดีโอ “CNV analysis with Daniel Howrigan” เป็นสื่อ คำที่ถูกพูดซ้ำบ่อยที่สุด: cnv, file, test, deletion, plink วิดีโอนี้มี 196 ประโยค และ 2593 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 19:53 ผู้พูดพูดด้วยความเร็วสม่ำเสมอ ประมาณ 130 คำต่อนาที เหมาะกับการฝึกพูดตาม มีเพียง 77% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| frequency คำนาม | /ˈfɹiːkwənsi/ | ความถี่ |
| variant คำนาม | /ˈvæɹi.ənt/ | การเปลี่ยนแปลง |
| burden คำนาม | /ˈbɝdn̩/ | ความรับผิดชอบ, ภาระ |
| gene คำนาม | /d͡ʒiːn/ | หน่วยพันธุกรรม |
| filter คำนาม | /ˈfɪltɚ/ | กรอง, เครื่องกรอง |
| output คำนาม | /ˈaʊtpʊt/ | เอาท์พุต |
| schizophrenia คำนาม | /ˌskɪtsəˈfɹiniə/ | โรคจิตเภท |
| sequence คำนาม | /ˈsiː.kwəns/ | ลำดับ |
| collapse คำกริยา | /kəˈlæps/ | พัง, ถล่ม |
| rescue คำกริยา | /ˈɹɛs.kjuː/ | ช่วยเหลือ, กู้ภัย |
| select คำกริยา | /sɪˈlɛkt/ | คัดเลือก |
| capture คำกริยา | /ˈkæp.(t)ʃɚ/ | ยึด |
| summary คำนาม | /ˈsʌm.ə.ɹi/ | สรุป |
| mechanism คำนาม | /ˈmɛk.ə.nɪ.zəm/ | กลไก, เครื่อง |
| dash คำนาม | /ˈdæʃ/ | ยัติภาค, ยัติภังค์ |
กริยาวลีที่คุณจะได้ยิน
| คำศัพท์ | ความหมาย |
|---|---|
| go back คำกริยา | กลับไป |
ประโยคที่ควรฝึกพูดซ้ำ
ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:
- I've listed a few here.
- How do we detect it using genotype data?
- I've also put down some sites.
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | I've listed · you've run · have changed |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | isn't changed · is given · are not forced |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 35 ครั้ง เช่น I've, I'm, don't ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “sh” และ “zh”: variation /ˌvɛəɹiˈeɪʃn̩/, structural /ˈstɹʌk(t͡)ʃəɹəl/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, dash /ˈdæʃ/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: schizophrenia /ˌskɪtsəˈfɹiniə/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, subsequently /ˈsʌb.sɪ.kwənt.li/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: structural /ˈstɹʌk(t͡)ʃəɹəl/, sequence /ˈsiː.kwəns/, collapse /kəˈlæps/, dash /ˈdæʃ/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: variant /ˈvæɹi.ənt/, variation /ˌvɛəɹiˈeɪʃn̩/, overlap /ˌəʊvəˈlæp/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
- /z/ — เสียงก้อง ไม่ใช่ /s/: mechanism /ˈmɛk.ə.nɪ.zəm/, whereas /(h)wɛɹˈæz/, browser /ˈbɹaʊ.zəɹ/, analyze /ˈæn.əˌlaɪz/, residual /ɹɪˈzɪd͡ʒ.u.əl/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- พูดตามทีละประโยคด้วยความเร็วปกติ ทำซ้ำแต่ละประโยคจนจังหวะของคุณตรงกับผู้พูด
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง frequency, variant, burden เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ